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Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment
As an alternative to the widespread method of a weighted sum of rewards, Explore Options let the agent call an intrinsically motivated agent in order to observe and learn from interesting behaviors in the environment.
Learning Intrinsically Motivated Options to Stimulate Policy Exploration
A Reinforcement Learning (RL) agent needs to find an optimal sequence of actions in order to maximize rewards.
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